843 research outputs found

    Creativity: Avalanche in the Sand-pile

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    [A revised and updated version of an earlier article 'Understanding Creativity: Affect Decision and Inference' (unpublished), posted at PhilArchive in 2021.] This book looks at the creative process in the human mind. Creativity involves a major restructuring of the conceptual space where a sustained inferential process eventually links remote conceptual domains, thereby opening up the possibility of a large number of new correlations between remote concepts by a cascading process. Since the process of inductive inference depends crucially on decisions at critical junctures of the inferential chain, it becomes necessary to examine the basic mechanism underlying the making of decisions. In the framework that we attempt to build up for the understanding of scientific creativity, this role of decision making in the inferential process assumes central relevance. Referring to the process of inferential exploration of the conceptual space that generates the possibility of correlations being established between remote conceptual domains, such exploration is guided and steered at every stage by the affect system, While the affect system plays a guiding role in the exploration of the conceptual space, the process of exploration itself consists of the establishment of correlations between concepts by means of beliefs and heuristics, the self-linked ones among the latter having a special role in making possible the inferential journey along alternative routes whenever the shared rules of inference become inadequate. Representing the conceptual space in the form of a complex network, the overall process can be likened to one of self-organised criticality commonly observed in the dynamical evolution of complex systems. An instance of self-organised criticality is found in the avalanche set up in a slowly growing sand-pile

    Complex systems methods characterizing nonlinear processes in the near-Earth electromagnetic environment: recent advances and open challenges

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    Learning from successful applications of methods originating in statistical mechanics, complex systems science, or information theory in one scientific field (e.g., atmospheric physics or climatology) can provide important insights or conceptual ideas for other areas (e.g., space sciences) or even stimulate new research questions and approaches. For instance, quantification and attribution of dynamical complexity in output time series of nonlinear dynamical systems is a key challenge across scientific disciplines. Especially in the field of space physics, an early and accurate detection of characteristic dissimilarity between normal and abnormal states (e.g., pre-storm activity vs. magnetic storms) has the potential to vastly improve space weather diagnosis and, consequently, the mitigation of space weather hazards. This review provides a systematic overview on existing nonlinear dynamical systems-based methodologies along with key results of their previous applications in a space physics context, which particularly illustrates how complementary modern complex systems approaches have recently shaped our understanding of nonlinear magnetospheric variability. The rising number of corresponding studies demonstrates that the multiplicity of nonlinear time series analysis methods developed during the last decades offers great potentials for uncovering relevant yet complex processes interlinking different geospace subsystems, variables and spatiotemporal scales

    Predicting extreme events in a data-driven model of turbulent shear flow using an atlas of charts

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    Dynamical systems with extreme events are difficult to capture with data-driven modeling, due to the relative scarcity of data within extreme events compared to the typical dynamics of the system, and the strong dependence of the long-time occurrence of extreme events on short-time conditions.A recently developed technique [Floryan, D. & Graham, M. D. Data-driven discovery of intrinsic dynamics. Nat Mach Intell 4\textbf{4}, 1113-1120 (2022)], here denoted as Charts and Atlases for Nonlinear Data-Driven Dynamics on Manifolds\textit{Charts and Atlases for Nonlinear Data-Driven Dynamics on Manifolds}, or CANDyMan, overcomes these difficulties by decomposing the time series into separate charts based on data similarity, learning dynamical models on each chart via individual time-mapping neural networks, then stitching the charts together to create a single atlas to yield a global dynamical model. We apply CANDyMan to a nine-dimensional model of turbulent shear flow between infinite parallel free-slip walls under a sinusoidal body force [Moehlis, J., Faisst, H. & Eckhardt, B. A low-dimensional model for turbulent shear flows. New J Phys 6\textbf{6}, 56 (2004)], which undergoes extreme events in the form of intermittent quasi-laminarization and long-time full laminarization. We demonstrate that the CANDyMan method allows the trained dynamical models to more accurately forecast the evolution of the model coefficients, reducing the error in the predictions as the model evolves forward in time. The technique exhibits more accurate predictions of extreme events, capturing the frequency of quasi-laminarization events and predicting the time until full laminarization more accurately than a single neural network.Comment: 9 pages, 7 figure

    Unstable Periodic Orbits: a language to interpret the complexity of chaotic systems

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    Unstable periodic orbits (UPOs), exact periodic solutions of the evolution equation, offer a very powerful framework for studying chaotic dynamical systems, as they allow one to dissect their dynamical structure. UPOs can be considered the skeleton of chaotic dynamics, its essential building blocks. In fact, it is possible to prove that in a chaotic system, UPOs are dense in the attractor, meaning that it is always possible to find a UPO arbitrarily near any chaotic trajectory. We can thus think of the chaotic trajectory as being approximated by different UPOs as it evolves in time, jumping from one UPO to another as a result of their instability. In this thesis we provide a contribution towards the use of UPOs as a tool to understand and distill the dynamical structure of chaotic dynamical systems. We will focus on two models, characterised by different properties, the Lorenz-63 and Lorenz-96 model. The process of approximation of a chaotic trajectory in terms of UPOs will play a central role in our investigation. In fact, we will use this tool to explore the properties of the attractor of the system under the lens of its UPOs. In the first part of the thesis we consider the Lorenz-63 model with the classic parameters’ value. We investigate how a chaotic trajectory can be approximated using a complete set of UPOs up to symbolic dynamics’ period 14. At each instant in time, we rank the UPOs according to their proximity to the position of the orbit in the phase space. We study this process from two different perspectives. First, we find that longer period UPOs overwhelmingly provide the best local approximation to the trajectory. Second, we construct a finite-state Markov chain by studying the scattering of the trajectory between the neighbourhood of the various UPOs. Each UPO and its neighbourhood are taken as a possible state of the system. Through the analysis of the subdominant eigenvectors of the corresponding stochastic matrix we provide a different interpretation of the mixing processes occurring in the system by taking advantage of the concept of quasi-invariant sets. In the second part of the thesis we provide an extensive numerical investigation of the variability of the dynamical properties across the attractor of the much studied Lorenz ’96 dynamical system. By combining the Lyapunov analysis of the tangent space with the study of the shadowing of the chaotic trajectory performed by a very large set of unstable periodic orbits, we show that the observed variability in the number of unstable dimensions, which shows a serious breakdown of hyperbolicity, is associated with the presence of a substantial number of finite-time Lyapunov exponents that fluctuate about zero also when very long averaging times are considered

    Transforming electrical energy systems towards sustainability in a complex world: the cases of Ontario and Costa Rica

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    Electrical energy systems have been major contributors to sustainability-associated effects, positive and negative, and therefore are considered as key components in pursuing overall sustainability objectives. Conventional electrical energy systems have delivered essential services for human well-being and can play a key role in tackling ongoing threats including growing poverty, climate change effects, and the long-term impacts of the COVID-19 pandemic. At the same time, some participants in electrical energy systems at national and local scales have stressed that the conventional design of electrical energy systems requires change to ensure the positive contributions and to reduce socioeconomic and environmental risks. Continuing negative trends including significant contributions to climate change, rising energy costs, deepening inequities, and long-term environmental degradation, have raised concerns and prompted calls for transforming conventional electrical energy systems rapidly and safely. However, due in part to the complexity of electrical energy systems, national and local authorities have struggled to steer their systems towards delivering more consistently positive sustainability-associated effects. Usual approaches to electrical energy system management have sought to improve efficiency, reliability and capacity to meet anticipated demand. They have seldom treated electrical energy systems as potentially important contributors to overall sustainability in principle and in practice. Doing so would entail recognizing electrical energy systems as complex systems with interlinked effects and aiming to maximize the systems’ positive and transformative effects to deliver multiple, mutually reinforcing and overall sustainability gains. The research reported here considered whether and how sustainability-based assessments can be useful tools to fill this gap and advance sustainability objectives in particular plans, projects, and initiatives carried out in electrical energy systems. To aid in responding the main research questions, this dissertation builds and proposes a sustainability-based assessment framework for electrical energy systems that is suitable for application with further specification to the context of different jurisdictions. Use of the framework is illustrated and tested through two case applications – to the electrical energy systems of Ontario and Costa Rica. Building the proposed framework involved a literature review and synthesis of three foundational bodies of knowledge: sustainability in complexity, electrical energy systems and sustainability, and transformations towards sustainability. Further specifying and applying the framework to the context of the two case studies involved carrying out document research and semi-structured interviews with key participants in the electrical energy systems of the two jurisdictions. The resulting sustainability-based assessment framework from this dissertation proposes six main criteria categories that are mutually reinforcing and emphasize minimizing trade-offs scenarios. These are divided into a set of criteria for specification and application to electrical energy system-related projects, plans, and initiatives in different regions. The proposed criteria categories are 1) Climate safety and social-ecological integrity; 2) Intra- and inter-generational equity, accessibility, reliability, and affordability; 3) Cost-effectiveness, resource efficiency and conservation; 4) Democratic and participatory governance; 5) Precaution, modularity and resiliency; and 6) Transformation, integration of multiple positive effects, and minimization of adverse effects. Ontario’s electrical energy system has significant sustainability-related challenges to overcome. The case study has shown that there is little provincial interest in following national net-zero commitments and authorities have removed official requirements for long-term energy planning to pursue climate goals and related sustainability objectives. Rising electricity prices have also raised concerns for many years and have been accompanied by limited willingness to engage in democratic and participatory processes for public review of electrical energy system undertakings. Additionally, recent commitments to highly expensive and risky options can further aggravate long-term socioeconomic and environmental negative impacts. In the Costa Rica case, adopting technocentric approaches to electrical energy system management led to a path dependency on large hydroelectricity development. This background of development of large hydroelectricity projects, without public consultation, has also created a sustained context of tension between governments, Indigenous groups and local communities, and private actors. Since the country is expected to experience changes in natural systems’ patterns including intensified periods of hurricane, storm, flood, and drought, the strong reliance on hydroelectricity has at the same time raised concerns regarding the reliability of the national electrical energy system. Both Ontario and Costa Rica have electrical energy systems that require rapid responses to contribute more positively to sustainability, and to help to reduce and reverse ongoing social and environmental crises. The two cases are also suitably contrasting venues for specification and application of the sustainability-based assessment framework developed in this work. The findings showed that while Ontario and Costa Rica have different contextual characteristics (e.g., geographical, socioeconomic, and political), overall lessons can be learned for best designing electrical energy systems in different jurisdictions. The findings also revealed that context-specific sustainability approaches do not necessarily undermine the viability for comparing multiple cases. In fact, specification to context can support comparisons by facilitating the identification of similarities and differences that are closely tied to contextual characteristics. Overall, the study of the two cases indicates significant potential for future works that focus on the specification to context and application of sustainability-based assessments specified to electrical energy systems that seek for barriers and opportunities for unlocking transformative effects. Three key learnings were revealed by building, specifying to context, and applying the sustainability-based assessment framework in a comparative analysis of the electrical energy systems of Ontario and Costa Rica. First, the two jurisdictions require implementation of more effective options to minimize costs in electrical energy system operations and avoid economic risks that undermine the capacity of the system to provide affordable electricity for all. Second, efforts to meet democratic and participatory governance requirements have been insufficient in Ontario and Costa Rica. Both jurisdictions need to demonstrate the capacity to respect official processes for public approval and to ensure adequate representation of different actors’ interests. Particularly, Indigenous people, local communities, and other groups with limited influence need more meaningful inclusion in official decision-making. Third, the two jurisdictions would benefit from implementing strategies to identify and assess possible combinations of policy and technical pathways that could help to unlock an existing dependency on options that support system rigidity. The core overall conclusion is that application of the proposed sustainability-based assessment framework can inform better design electrical energy systems to deliver broader sustainability-related effects and advance transformations towards sustainability. However, the framework could be further developed by including insights from more key participants in electrical energy systems. The criteria set can be honed with specification to context and application to different jurisdictions, and to more particular initiatives that reflect evolving energy scenarios. Inclusion of transformation, integration of multiple positive effects, and minimization of adverse effects as a criteria category has been helpful to recognize political contexts, promote just transitions, and emphasize the interlinked effects of applying the rest of the criteria. Since this is a new component in sustainability-based assessment frameworks, the transformation criteria category will require particular attention in future applications. Among other matters, further work in the field of electrical energy systems transformation towards sustainability should also address continuing and emerging phenomena, including adverse political trends such as right-wing populism and post-truth politics, that would maintain gaps between current practices and the steps needed for progress towards sustainability. Generally, however, while there are many needs and opportunities for more applications of the framework and additional research into the barriers to and openings for energy system transition and transformation, the sustainability-based assessment framework proposed and tested in this dissertation research should be a useful tool for directing change in complex electrical energy systems towards broader contributions to sustainability

    Collective behavior of biological aggregates

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    Mención Internacional en el título de doctorThe behavior of living systems can exhibit emergent phenomena that are not present in their individual components. Living systems are far from equilibrium, may lack conservation laws and using tools from statistical physics and thermodynamics is challenging. However, quantitative observations and experiments yield a wealth of data that allow to design models capturing important aspects of the behavior of these systems. This thesis focuses on two topics, the collective behavior of midge swarms and the collective behavior of bacterial biofilms. Prior studies of insect swarms have focused on their formation as an ordering phase transition, which fails to capture many qualitative and quantitative features. In this thesis we study the Vicsek model confined by a harmonic potential. By using dynamical systems and statistical mechanics tools, we have discovered a novel phase transition characterized by scale free chaos, which exhibits power laws and present qualitative features compatible with observations of swarms of male midges in nature and in the laboratory. On the second topic, bacterial biofilms pose a challenge to theorists, who must model elements on different spatial and temporal scales. We present a hybrid model based on an architecture of immersed boundaries with a dynamic energy budget metabolism. The model captures geometric differences between bacteria, being able to reproduce varied patterns depending on their shapes and competence phenomena between different types. We can study antibiotic resistance in biofilms and test cocktails to eradicate them.El comportamiento de los sistemas vivos puede mostrar fenómenos emergentes que no están presentes en sus componentes individuales. Los sistemas vivos están alejados del equilibrio, pueden carecer de leyes de conservación y es todo un reto emplear herramientas de la física estadística y la termodinámica. Experimentos y observaciones cuantitativas producen una gran cantidad de datos que permiten diseñar modelos que expliquen aspectos importantes del comporamiento de estos sistemas. Esta tesis se centra en dos temas, el comportamiento colectivo de los enjambres de insectos voladores como los mosquitos y el comportamiento colectivo de las biopelículas bacterianas. Los estudios previos de enjambres de insectos se han centrado en su formación como una transición orden-desorden, lo que no explica muchos aspectos tanto cualitativos como cuantitativos. En esta tesis estudiamos el modelo de Vicsek confinado por un potencial armónico. Usando herramientas de sistemas dinámicos y de mecánica estadística hemos descubierto una nueva transición de fase caracterizada por caos libre de escalas, que presenta leyes de potencias y tiene rasgos cualitativos compatibles con las observaciones y experimentos de laboratorio de enjambres de dípteros. Por otro lado, las biopelículas bacterianas plantean un reto teórico, pues se debe modelar elementos a diferentes escalas espaciales y temporales. Presentamos un modelo híbrido basado en una arquitectura de fronteras inmersas con un metabolismo de presupuesto de balance energético. El modelo captura las diferencias geométricas entre bacterias, generando patrones diversos según su forma y competencia entre bacterias de distintos tipos. Permite estudiar la resistencia de biopelículas a antibióticos y el diseño de cócteles para erradicarlos.Agradezco el apoyo del Ministerio de Economía y Competitividad de España a través de las ayudas del programa de Formación de Doctores, PRE2018-083807, cofinanciada por el Fondo Social Europeo. También agradezco al apoyo de FEDER /Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación por medio de los proyectos MTM2017-84446-C2-1-R y PID2020-112796RB-C21.Programa de Doctorado en Ingeniería Matemática por la Universidad Carlos III de MadridPresidente: Björn Birnir.- Secretaria: Ester Aurora Torrente Orihuela.- Vocal: Antonio Prados Montañ

    Trends in recurrence analysis of dynamical systems

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    The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g. for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Application of spin glass ideas in social sciences, economics and finance

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    Classical economics has developed an arsenal of methods, based on the idea of representative agents, to come up with precise numbers for next year's GDP, inflation and exchange rates, among (many) other things. Few, however, will disagree with the fact that the economy is a complex system, with a large number of strongly heterogeneous, interacting units of different types (firms, banks, households, public institutions) and different sizes. Now, the main issue in economics is precisely the emergent organization, cooperation and coordination of such a motley crowd of micro-units. Treating them as a unique ``representative'' firm or household clearly risks throwing the baby with the bathwater. As we have learnt from statistical physics, understanding and characterizing such emergent properties can be difficult. Because of feedback loops of different signs, heterogeneities and non-linearities, the macro-properties are often hard to anticipate. In particular, these situations generically lead to a very large number of possible equilibria, or even the lack thereof. Spin-glasses and other disordered systems give a concrete example of such difficulties. In order to tackle these complex situations, new theoretical and numerical tools have been invented in the last 50 years, including of course the replica method and replica symmetry breaking, and the cavity method, both static and dynamic. In this chapter we review the application of such ideas and methods in economics and social sciences. Of particular interest are the proliferation (and fragility) of equilibria, the analogue of satisfiability phase transitions in games and random economies, and condensation (or concentration) effects in opinion, wealth, etcComment: Contribution to the edited volume "Spin Glass Theory & Far Beyond - Replica Symmetry Breaking after 40 Years", World Scientific, 202
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